System and method for visual recognition

ABSTRACT

A method for visual recognition of an object in an electronic image includes extracting unique points of an object to be learned and/or a target object. The unique points are obtained by cross-correlating the image with a structure. Generally, the structure and/or the size of the structure may vary to detect extremum information associated with the learned object and/or target object. An icon corresponding to each of the unique points is extracted. The size of the icon corresponds to the scale of the unique point. After extraction of the various icons, an object becomes a collection of icons. Each of these icons is un-rotated and normalized or resized to a constant size so it can be compared with other icons.

RELATED APPLICATION DATA

This is a continuation application of application Ser. No. 12/101,583,filed on Apr. 11, 2008 and issued as U.S. Pat. No. 8,150,165 on Apr. 3,2012.

TECHNICAL FIELD OF THE INVENTION

The present invention relates to the field of computer vision, and moreparticularly, to a system and method for visual recognition for use in awide variety of applications.

DESCRIPTION OF THE RELATED ART

Computer vision generally relates to the theory and technology forbuilding artificial systems that obtain information from images ormulti-dimensional data. As used herein “information” means anything thatenables a decision to be fully and/or partially based. Exemplarycomputer vision applications include: visual object recognition andscene interpretation, particularly for image retrieval, video indexing,controlling processes (e.g. an industrial robot or autonomous vehiclesuch as unmanned aerial/ground/see vehicle), detecting events (e.g. forvisual surveillance), organizing information (e.g. for indexingdatabases of images and image sequences), Image based internet search(e.g., searching for similar image on the Internet), modeling objects orenvironments (e.g. medical image analysis or topographical modeling),interaction (e.g. as the input to a device for computer-humaninteraction), etc.

A goal of computer vision is to make a computer truly “see” just likehumans do. Understanding the content of everyday images and videos isone of the fundamental challenges of computer vision. In order to make acomputer “see” in an unconstrained environment an extraordinary amountof computational power, perhaps on the order of 10¹⁵ operations persecond likely is needed. Even if such a speed was possible in acommercial computer vision system, it is difficult to perform rapidvisual searches in unconstrained, natural environments.

To make search and recognition tasks tractable in commercial computervision, designers typically limit the task's visual complexity. This maybe done in a variety of ways. For example, the vision system may be setup to view and recognize only one or a small class of objects. Second,the presentation (position, orientation, size, view, etc.) of theseobjects is strictly controlled. Thus, the object variability is limitedto the point that the vast majority of variables are eliminated and thesearch can be implemented with reasonable cost in terms of bothcomputing time and money.

Computer vision systems generally lack the knowledge needed to constrainand interpret a general visual search (e.g., searches performed in anuncontrolled environment). Therefore, practical computer vision searchrequires the designer to drastically restrict what the vision systemsees and to add a priori knowledge about what it will see so that it caninterpret the result. Thus, a major drawback to computer vision in realworld applications is the time, money and specialized knowledge neededfor such applications to be adequately performed.

The evolution of computer vision in the last twenty years was driven byimprovements in hardware and algorithms. A variety of computer visionmethods have been developed for image detection (also referred to hereinas pattern recognition). These techniques include, for example, usingbinary images to represent gray scale images, normalized grayscalecorrelation, blob analysis, geometric based search and recognition,contour based search, affine invariant constellation based recognition,corner detection, salient icon detection, scale invariant featuretransform, etc.

SUMMARY

A strong need exists in the art of computer vision to recognize objectsin an image or image sequence similar to vision in human beings. Forexample, in an airport, an unmanned vehicle needs to recognize othervehicles and obstacles so it can avoid and/or maneuver through theairport. In an unmanned vehicle or other robotic vision application, therobotic application generally needs to “see” the pathway and navigateautonomously or land autonomously.

In visual recognition, achieving invariance to object presentation(position, orientation, distance (scale), and perspective), lighting,occlusion and background is challenging. Aspects of the presentinvention provide excellent invariance to object presentation, lighting,occlusion and background and generalization for true object recognition.

The human brain processes visual information associated with objectswith full independency of the position, orientation, distance (scale),and perspective. For example, if a human being views a “soda pop can”,the human can recognize it regardless of the distance and/or orientation(e.g., distance from can, rotation, tipped, tilted, etc.). The brainessentially “normalizes the view”. Humans are capable of learning alarge number of objects and easily retrieve the learned objects. Aspectsof the present invention allow learning virtually an unlimited number ofobjects and recognizing any one of these learned object(s) regardless ofobject presentation. This is analogous to human visual recognitioncapability. For example, aspects of the invention enables therecognition of hundreds of trained objects very quickly (e.g., in lessthan a second) and fundamentally has no limit in learning andrecognizing millions of objects. This capability stems from the abilityto extract the same icons (image patches) from an image of an objectregardless of distance, rotation, presentation that the object is inrelation to the viewer and/or the device acquiring the image or seriesof images.

Aspects of the invention relate to extracting unique points (e.g., x andy coordinate points) in an image. Each one of these unique points hasits own unique scale (e.g., size) and orientation that is relateddirectly to the presentation of the object. Having scale and orientationinformation measured per unique point enables visual recognition that isfully invariant to presentation. In other words, when an object iscloser, farther, rotated, tipped, and/or tilted, these unique pointshave similar relative locations to the object and a unique scale that isrelated to how close/far the object is and rotation values that arerelated directly to the object planar rotation. Basically these uniquepoints “normalize the view” of the object.

An icon (image patch) from an image of an object is extracted from eachof these unique points. The size of the icon corresponds to the scale ofthe unique point. And the angle of the icon is the angle of the uniquepoint. After extraction of the various icons, an object becomes acollection of icons. Each of these icons is un-rotated by icon angle andresized to a constant size so it can be compared (distance measure suchas absolute difference) one-to-one with other icon (also referred toherein as “normalized”. It has been determined that the icons arevirtually identical regardless of object presentation. In other words,the icons (image patches) are the same whether the object is close orfar, rotated, tilted, and/or tipped. One of the unique properties ofthese icons is their stability over scale and angle. Comparing an iconfor similarity may also include color information. Generally, whencomparing two icons, each icon may also be intensity-normalized.

Searching for an object in database of learned object's images becomes asearch of vectors associated with learned object's images. Indexingtechniques are one way represent an image for searching.

Computing geometric transformation between a learned object and a foundobject is done by computing the transformation between the correspondinglearned icon's position and found icon's position, as discussed below.The transformation matrix between learned object and found object iscomputed using a perspective matrix using least square of allcorresponding icons positions or by picking two sets of quad iconsposition from the learned and found objects. Based on rigid bodyassumptions, every set of four icons can compute a perspective matrix.Many sets of four icons give the same transformation, which provides arobust measure of correct match, also referred to herein as, measureredundancy, as discussed below.

One aspect of the present invention relates to a method for visualrecognition of at least one object in an image, the method comprising:providing an image in an electronic format, wherein the image includesat least one object to be learned; generating extremum informationassociated with the image by cross-correlating at least one structureacross at least a portion of the image, wherein the extremum informationincludes at least one coordinate point associated with cross-correlatingthe at least one structure across the image; extracting at least oneicon from the image, wherein the icon includes the coordinate pointassociated with the extremum information; determining an angleassociated with the at least one icon; normalizing the icon to a fixedsize; and storing icon information in a computer readable form, whereinthe icon information includes image values associated with at least aportion of the icon; the at least one coordinate point associated withthe extremum information; and the angle associated with the at least oneicon.

Another aspect of the invention relates to a method for matching alearned object with a target object, the method comprising: providing atleast one learned object and at least one target object, wherein thelearned object and the target object; extracting unique points from thetarget object, wherein the unique points are generated from extremuminformation obtained from the target image by cross-correlating at leastone structure across the target image; extracting an icon of the targetimage corresponding to each of the unique points; determining an angleassociated with the at least one icon; normalizing the extracted icon;and determining if the extracted icon from the target images matches alearned object.

Another aspect of the invention relates to a program stored on a machinereadable medium, the program being suitable for use in matching alearned object with a target object, wherein when the program is loadedin memory of an associated computer and executed, causes extractingunique points from the target object, wherein the unique points aregenerated from extremum information obtained from the target image bycross-correlating at least one structure across the target image;extracting an icon of the target image corresponding to each of theunique points; determining an angle associated with the at least oneicon; normalizing the extracted icon; and determining if the extractedicon from the target images matches the learned object.

Other systems, devices, methods, features, and advantages of the presentinvention will be or become apparent to one having ordinary skill in theart upon examination of the following drawings and detailed description.It is intended that all such additional systems, methods, features, andadvantages be included within this description, be within the scope ofthe present invention, and be protected by the accompanying claims.

It should be emphasized that the term “comprise/comprising” when used inthis specification is taken to specify the presence of stated features,integers, steps or components but does not preclude the presence oraddition of one or more other features, integers, steps, components orgroups thereof.”

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other embodiments of the invention are hereinafterdiscussed with reference to the drawings. The components in the drawingsare not necessarily to scale, emphasis instead being placed upon clearlyillustrating the principles of the present invention. Likewise, elementsand features depicted in one drawing may be combined with elements andfeatures depicted in additional drawings. Moreover, in the drawings,like reference numerals designate corresponding parts throughout theseveral views.

FIGS. 1A-1D is an exemplary illustration of a structure and correlationmethod in accordance with aspects of the present invention.

FIG. 2 is an exemplary illustration of a structure having various scalesin accordance with aspect of the present invention.

FIGS. 3A-3N illustrate correlation results and corresponding extremuminformation associated therewith in accordance with aspects of thepresent invention.

FIGS. 4A and 4B illustrate icon angle vectors in accordance with aspectsof the present invention.

FIG. 5 is an exemplary illustration of normalized icons obtained inaccordance with aspects of the present invention.

FIGS. 6A and 6B illustrate exemplary icons in accordance with aspects ofthe present invention.

FIGS. 7A-7C illustrate exemplary icons in accordance with aspects of thepresent invention.

FIGS. 8A-8C illustrate exemplary structures in accordance with aspectsof the present invention.

FIG. 9-13 are exemplary methods in accordance with aspects of thepresent invention.

FIGS. 14A-14B illustrate exemplary objects having different scales andorientations in accordance with aspects of the present invention.

FIG. 15 is a block diagram of a system in accordance with aspects of thepresent invention.

DETAILED DESCRIPTION OF EMBODIMENTS

The present invention is directed to a system and method for patternidentification of a learned image (or learned pattern) in a targetimage. Unique and stable points (e.g., x and y coordinate points) areextracted from an image of an object, regardless of object presentation.The uniqueness of the extracted points comes from the fact that thesepoints have the same relative position in the object regardless ofdistance, orientation (e.g., tip, tilt, rotation, etc.) and illuminationof the object from the viewer or viewing device. In other words, theextracted points are invariant to object presentation.

Early computer vision algorithms generally used an image subtractionmethod (also referred to as golden template matching) as a primitivemethod of recognition. Image subtraction is a form of distance measurebetween two images. For the image subtraction method to work, the objectin the learned image has to be nearly identical to the object in thetarget (scene) image. For example, the object generally has to be in thesame position, same scale, same planar angle, etc. as the learnedobject. Any shift in location or other transformations would produce afalse result. Such conditions were generally needed because thesubtraction method simply subtracted pixel values having coordinates inone image with pixel values located at corresponding coordinates inanother image.

If a method can find regions in an object image that are the sameregardless of object presentation, it is conceivable that the goldentemplate concept may be used to recognize regions of that object, whichcould result in recognizing the entire object. Prior methods to findthese unique points and associated icons produced either unstable pointsin position, unstable scale, and/or unstable angle. The prior methodsproduced few inliers and majority of outliers, which makes patternrecognition generally problematic. Researchers in academia haveexperimented with several types of these unique points. Example of someof the famous type of these points are, Harris-Corner-Detector,Harris-Laplace, Laplacian-of-Gaussian, SIFT (Difference of Gaussian).Generally these detectors lack stability in position, scale and angle,which produces various problems during the recognition process. Forexample, these prior methods produce hundreds and/or thousands of uniquepoints and only a handful of such points may survive from one objectposition to another to aid in pattern recognition.

An invariant point in an image generally needs to have thecharacteristic of extremum for some metric. For example, in a onedimensional signal, such as a parabola, the peak point of a parabola isan invariant point regardless to the parabola's parameters (e.g.,regardless to how wide, narrow, shifted, or rotated a parabola is).

Referring to FIGS. 1A-1D, an exemplary method 10 of extracting uniqueand highly stable points (e.g., x and y coordinate points) isillustrated. These points are highly stable in position, scale, andangle. Accordingly, such points are referred to herein as scale rotationinvariant (SRI) points (and/or SRIP). One way of extracting the SRIpoints is by cross correlating the object image by a structure 12.Cross-correlation is generally a measure of the similarity of twosignals. Cross-correlation is commonly used to find features in anunknown signal by comparing it to a known one. It is a function of therelative time between the signals and is sometimes called the slidingdot product.

Structure 12 may be circular, conic or Gaussian shape. In oneembodiment, the structure 12 is a cone-like structure. Referring to FIG.1A, the cone-like structure 12 is illustrated in two dimensions.

The cone-like structure 12 has a cone angle θ that generally correspondsto the height (h) of the structure 12. For example, SRI points generallyhave very interesting properties that aid in recognition. Based oncorrelation score between cone-like structure (or any other desiredstructure) and the image: a correlation score close to 1.0 identifiesround structures; a correlation score of about 0.5 identifies strip likestructures; and a correlation score of about 0.7 identifies an end ofstrip like structure. One of ordinary skill in the art will readilyappreciate that the shape of the structure chosen may correspond to achange in correlation score for the identified structure.

Referring to FIG. 1B, a “steel plate” 14 is illustrated. The steel plate14 has holes 16 of varying sizes along a surface 18. The steel plate isanalogous to the object image or scene image.

The structure 12 is attempted to be “inserted” into the steel plate 14in a scanned manner (e.g., one pixel after another pixel). As shown inFIG. 1C, the cone-like structure 12 is illustrated being inserted into a“steel plate” 14, for purposes of illustration. One goal is to findholes (or other meaningful structure) by poking (in a scanned way)(i.e., pixel by pixel and/or group of pixels by group of pixels) thestructure 12 (e.g., a cone-shape pin) into the steel plate. Thestructure 12 (e.g., the cone-like shape is the known cross-correlationimage) is used to generate the unique points. If a point on the surface18 does not contain a hole, the pin does not go into the steel plate 14,which yields a low score correlation match (e.g., see “D” in FIG. 1C).Referring to FIG. 1C, if structure 12 is inserted (or poked) into ahole, the cone would go in some distance depending on the pin conic sizeand hole size in the plate. For example at “A”, the structure 12 extendsinto the hole 16 approximately half-way. At “C”, the structure 12, whichis substantially the same size as the structure at “A”, extends almostfully into the hole. Accordingly, “C” has a higher correlation than “A”.Also, note that at “B”, a larger structure 12 than was used at “A” and“C” is illustrated. The larger structure extends approximately 80% intothe hole 16, at “B”. If the same sized structure used at “A” and “C”were inserted into the hole at “B”, the structure would hit the bottomwithout touching any of the walls associated with the hole 16 and,therefore, not have a strong correlation, no extremum. In oneembodiment, it is desirable to cross-correlate an image with structureshaving a variety of scales in order to identify all or at least aportion of the extremum information available on the image.

Referring to FIG. 1D, the amount that the cone goes inside (e.g.,correlation match score) is related to the structure, cone-angle, andthe size of the hole, or the structure. For example, this can be view asa two dimensional frequency analysis of an image. The result of crosscorrelating a cone structure with the image at a different scale is aset of coordinate points (x,y) and unique radius (scale) for each ofthese points. The radius value is directly related to the scale of theimage and to the size of the structure around the coordinate point.

FIG. 2 illustrates the cone-like structure 12 in a two-dimensional imagefor six different scales. The scale size may be any desirable size foreach of the various structures used to identify the SRI points. The SRIpoints for each of the various structure sizes will generally vary whencross-correlated with an image. The collection of SRI points for all ofthe structure sizes is generally used to define the object, as discussedbelow. For example, the scale size of the structure 12 may vary from apoint contact (e.g., 5×5 pixel) to the size of the image and/or objectof interest. The gradations in color change correspond to the height ofthe cone-like structure 12

FIGS. 3A-3G illustrate an exemplary image that has been cross-correlatedwith a structure 12 of varying cone size (scale) (e.g., cone-likestructures illustrated in FIGS. 1 and 2). The exemplary image may be anydigital image, a portion of an object or image, an electronicrepresentation of an image, etc. As shown, in FIGS. 3A-3G, the image isa digital image of an object. It may be desirable for a machine todetermine the precise location and/or orientation of one or more items(or patterns) in this scene. This information may be used in anydesirable manner. For example, the information may be used so that acontroller, a device, or other electronic device may properly interactwith software that is capable of detecting optical objects in order tofacilitate controlling, locating, assembling and/or processinginformation related to the item.

Once the image has been cross-correlated, a resulting set of uniquepoints (also referred to herein as x and y coordinate points) and radiusvalues (scale) for each of the points are obtained, as is illustrated bythe white circles in each of the images. As stated above, any structure12 may be used in accordance with aspects of the present invention. Forexample, a two dimensional Gaussian provided similar results as thecone-like structure. The cone-like structure 12 is utilized to findextremum in the object regardless of scale. This provides robust andreliable relative localization (e.g., x and y coordinate positions inthe image), and scale.

Once the correlation image is computed a peak detector is applied tofind the coordinate of peaks in sub pixel form, as illustrated in FIGS.3H-N. FIG. 3H identifies the extremum corresponding to FIG. 3A; FIG. 3Iidentifies the extremum corresponding to FIG. 3B; FIG. 3J identifies theextremum corresponding to FIG. 3C; FIG. 3K identifies the extremumcorresponding to FIG. 3E; FIG. 3L identifies the extremum correspondingto FIG. 3D; FIG. 3M identifies the extremum corresponding to FIG. 3F;and FIG. 3N identifies the extremum corresponding to FIG. 3G. Thecone-like structure 12 is a rotational invariant extremum detector forrange of scales. In addition, the cone-like structure 12 also offerssuperior position localization regardless of scale.

Once the unique points (e.g., coordinate values) are determined, an icon(image patch) is extracted from the associated at each of thesecoordinates. The icon size is proportional to the radius (scale) of thecone structure having a high cross-correlation match value. The angle ofthe icon 50 is computed from a vector between the unique point position54 and the grayscale centroid position 52 of the icon at scale, as shownin FIGS. 4A and 4B. As shown in FIG. 4A, icon angle computation is basedon the gray scale centroid using the raw image of the icon, for example.Icon angle computation may also be based on the edge detected image ofthe icon, as shown in FIG. 4B. The angle of the icon is the vector fromthe center of icon to the grayscale centroid. The grayscale centroid canalso be applied on the raw image (e.g., such as Sobel or Canny edgedetection).

Once extracted, each one of the icons is normalized to a fixed size, asshown in FIG. 5. FIG. 5 illustrates the set of icons extracted from theimage at each coordinate point for each scale associated with thestructure. For example, FIG. 5 illustrates a matrix of normalized icons.The purpose of normalizing these icons into constant size is to be ableto compare them with other icons (for the purpose of finding similaricons in database of icons, thus similar objects), by simply computingthe difference between the two images as in the “golden templatematching”. Another method to compare these icons is by creatingdescriptor vector and then comparing these descriptors using distancemeasure between vectors. For example, the icon of FIG. 6A may bedescribed in descriptor vector format as:6,17,22,23,16,0,0,11,3,26,25,3,0,0,20,20,6,5,4,10,5,60,0,7,6,14,4,12,9,22,12,16.Likewise, the icon of FIG. 6B may be described in descriptor vectorformat as:13,7,21,11,4,5,24,11,0,58,38,0,0,0,2,0,0,5,20,11,4,28,28,1,7,6,0,0,0,84,1,0.One of ordinary skill in the art will readily appreciate that there aremany ways to compute icon descriptors (e.g., histogram of gradientangle, principle component analyses (PCA), etc.).

Each learned object may be described by a set of icons. Each icongenerally includes one or more values, for example: (x,y) coordinateposition, a size that correspond to the size of the image structure fromwhich the icon originated from, and an angle. For example, FIGS. 7A-7Cillustrates various icons extracted from a correlation of structureshaving various sizes. The spatial relation of these icons is insured bythe outline of the object. These icons may be stored in any electronicstorage device. For example, the icons may be stored in a database oficons that generally includes an identifier, which is tagged and/orotherwise associated to a specific learned object. In anotherembodiment, a descriptor associated with each of the icons is stored ina database or other suitable data storage medium. In another embodiment,icons may also be extracted at multiple-scale values that producemultiple icons per unique points, as opposed to extracting icons only atthe cone-structure-scale. For example, if the cone-structure scale is32×32 pixels, then extract icons at 32×32 pixels and 48×48 pixels, asillustrated in FIG. 7C. This method generally guarantees truecorrespondence and recognition from a very few number of icons. In fact,in many situations only one unique icon may be needed to determinerecognition of the object.

FIGS. 8A-8C illustrate one process of extracting unique points from animage. In FIG. 8A, unique points are extracted along a strip like regionwith correlation score of about 0.5. FIG. 8B illustrates the end of astrip and has a correlation score of about 0.7. FIG. 8C illustrates manyround objects being extracted. The correlation score with the roundobjects is approximately 1, indicating that the round objects highlycorrelate with the structure (e.g., the cone-like structure) selectedfor cross-correlating.

FIG. 9 illustrates one exemplary method 100 for extracting scale androtation invariant icons from an image. At block 102, an image of anobject is provided in electronic form. The image may be in any suitableelectronic format (e.g. JPEG, TIFF, PDF, bitmap, etc.) At block 104, theimage of an object is cross-correlated with one or more structures 12(e.g., cone-like structures), as described above. At block 106, outputimage of cross-correlation operation is obtained for each of thecross-correlation structures. At block 108, peak values are extractedfor each of the cross-correlation structures. At block 110, a list ofcoordinate points per cross-correlation structure is obtained and storedin a memory.

FIG. 10 illustrates one exemplary method 120 for extracting scale androtation invariant icons from an image. At block 122, an image of anobject is provided in electronic form. At block 124, the list ofcoordinate point per cross-correlation structure is provided. At block126, the icon angle is generated for each of the icons and storedappropriately for later use at block 128. At block 130, the icons arethen normalized by appropriate scaling, as desired. At block 132, theicons are stored in a memory or other electronic storage device.

FIG. 11 illustrates another exemplary method 150 for extracting scaleand rotation invariant icons from an image. At block 152, an image of anobject is provided in electronic form. At block 154, scale rotationinvariant points are extracted at a scale. At block 156, a subscale iscomputed for each icon. An extremum point is usually a peak at a scaleand neighboring scales. Therefore, it is possible to compute subscale bytaking the peak value at “best scale” and its neighboring scale. Thescale of an icon becomes the scale of the cone-like structure plus orminus subscale. A well known method is parabola fit to find its peak insub-position.

At block 158, the icon for each for each of the coordinates is computedfor the scale. At block 160, given the icon angle and scale for each ofthe coordinates, extract icons from the image. At block 162, the iconsare normalized to a fixed size. At block blocks 154 through 160 arerepeated until all icons have been extracted. At block 164, a constant(K) of highly stable and invariant icons that represent the object areobtained and may be stored in a memory or other suitable storage deviceor pumped into an indexing data base or hash table.

An exemplary method 200 for learning an object is illustrated in FIG.12. At block 202, an image of an object is provided in electronic form.At block 204, an object contour point is extracted from an image of andsampled. The contours points may be used for hypotheses verification andto verify spatial relation between coordinates of unique points. Ingeneral, the unique points drive the attention and contour points verifythe hypotheses. Sample points from the contour points are selected,which provides a fast verification process.

At block 206, SRI points acquired from the image and/or object to belearned are used to extract icons associated with the SRI points. In oneembodiment, each icon has its (x, y) coordinate, size (scale) and angle.At block 208, a descriptor for each icon is created. In addition or inthe alternative, each icon may also be tagged or otherwise associatedwith a learned object name.

At block 210, similar icons are found and tagged. Similar icons aregenerally suitable for recognition, but not unique enough for locatingthe object unless the spatial relation between the icons is applied suchas, for example, nearest neighbor icon; n nearest neighbors; left, top,bottom, left neighbor; etc. Similar icons may have multiplecorrespondences. Blocks 202-210 are repeated for every object needed tobe learned. Once learned, an object becomes a collection of icons (ortheir descriptors) and the spatial relation that ties the icons togetheris a set of object contour points. The icon coordinates also can be usedfor detecting, determining and/or verifying special relationshipsbetween the icons.

Using descriptors provides a variety of advantages. Such advantagesinclude, for example, permitting the use of indexing techniques for fastretrieval of similar icons in a database of icons, which hastensretrieval of similar objects. This functionality is highly desirablewhen recognizing an object(s) from a large database of objects.

During the recognition phase, an object or multiple objects may exist inan image (scene image) and one goal is to recognize the object ormultiple objects and provide the x and y coordinates of each object. Anexemplary recognition method 250 is illustrated in FIG. 13. At block252, an image having one or more objects to identify is provided inelectronic form. At block 254, the recognition phase is initialized. Atblock 256, contour points are extracted from the image and SRI pointsare extracted from the image. At block 258, icons of the image areextracted at each of the unique points and normalized, as set describedabove. At block 260, for each extracted icon, a best matched icon isfound or otherwise searched for in a database of icons using either asequential method if number of learned object is small or an indexingmethod if the number of learned object is large.

At block 262, candidate objects in the image are identified and rankedbased on the quality of the match and/or the number of matches. At block264, the location of the object or objects is determined. At block 266,based on the learned icon or icons of an identified object or objects,corresponding icons in the recognized icons are found. This may beaccomplished by a variety of methods.

One exemplary method begins at block 268. At block 268, allcorresponding icons are used to compute a perspective transformation.This may be done by generating one or more hypotheses. The hypothesesmay be generated in any desired manner. For example, all icons oflearned object(s) may be selected, and compared with correspondingmatched icons. Using a least square method correlation method or anothercorrelation method, a perspective transform is generated between thelearned unique point's coordinates and the corresponding matched pointsin the scene image. A least squares correlation method is generallypreferred because there may be some outliers, at this juncture.

At block 270, the sampled learned contour points of candidate objectsare transformed and superimposed on the scene image for verification. Amatch score between sampled contour and scene contour is computed. Thetransformation having the best contour match is selected. At block 272,using the transformation of the best match, all unique points of thelearned object are transformed with the perspective transform onto theimage. In addition, the distance between these transformed points andscene object unique points are computed. At block 274, any outliercorresponding icons are removed and the transform having the best matchis saved for later use. Blocks 270 through 274 are repeated N times,where N is the number of corresponding icon points. Once this method hasbeen applied N times, the transform having the best match is saved forlater use.

Another method for obtaining precise localization of an icon isillustrated in blocks 280-286. Referring to block 280, for every quadset of corresponding icons points (e.g., coordinates), a perspectivetransform is computed using least squares or some other correlationmethod. At block 282, the object contour points with the perspectivetransform is transformed, in similar manner as block 270. At block 284,transformed contour points are transformed and superimposed onto theimage to verify the transform, as discussed above with respect to block272. At block 286, the transform producing the best match is saved andthe process repeats N times (where N is the number of quad sets).

FIGS. 14A and 14B illustrate samples of unique points that are invariantto presentation. For example, FIG. 14A illustrates an object in animage, wherein the image was taken at a relatively close distance to theobject and/or the image has been zoomed in around the object. FIG. 14Billustrates an image of the same object taken at a further distanceand/or zoomed further out, and rotated, tipped and tilted than the imagein FIG. 14A.

In operation, a method of generating the hypotheses picks apredetermined number of points (e.g., four points) of the leaned uniquepoints and a predetermined number of corresponding points (e.g., fourpoints) in the scene image. The process is repeated for N number of quadpoints. For every set of four points the perceptive transform isgenerated and sampled contour points are transformed and superimposed onthe scene contour points. The transform of highest match between learnedcontours and scene contour is kept as the best transformation transform.For a set of ten matching icons between learned object and found object,there are 210 possible combination and, of course, 210 possiblehypotheses generation and verifications. The speed of the recognitionprocess (the entire process) for one learned object is about 140millisecond using standard off the shelf Pentium based processor with1.6 GHz processor speed. It is approximately 2 milliseconds extra forevery learned object using sequential icon matching. This means, forexample, that for 430 learned objects, a recognition would take 1 second((1000−40)/2). A hash table based indexing would have the potential ofachieving recognition of a million objects in one second.

As a practical contribution, the aspects of the present invention may beused in a wide variety of application including, for example, Exemplarycomputer vision applications include: visual object recognition andscene interpretation, particularly for image retrieval, video indexing,controlling processes (e.g. an industrial robot or autonomous vehiclesuch as unmanned aerial/ground/see vehicle), detecting events (e.g. forvisual surveillance), organizing information (e.g. for indexingdatabases of images and image sequences), Image based internet search(e.g., searching for similar image on the Internet), modeling objects orenvironments (e.g. medical image analysis or topographical modeling),interaction (e.g. as the input to a device for computer-humaninteraction), applications wherein a closed-loop guidance and/or controlsystem is utilized that requires a fast searching algorithm, etc.

FIG. 15 illustrates an exemplary feedback system 300 that may be used inaccordance with the aspects of the present invention. The system 300 mayinclude an optical input device 302 (e.g., a CCD camera) and/or anelectronic storage device 304 for providing a learned image and/or atarget image to a processor 306. The output of the devices 302, 304 maybe input to a processor 306 that has computer code that is functional tocarry out the desired functionality. The processor 306 may generate acontrol signal to a controller 308 (e.g., programmable logic controller)that may be used to control one or more electronic devices 310 (e.g.,vehicle navigation system, tracking system, etc.). A feedback signal maybe generated by the electronic device 310 to the controller 308 and/orprocessor 306 in order to control the particular application in whichthe invention is being applied.

Computer program elements of the invention may be embodied in hardwareand/or in software (including firmware, resident software, micro-code,etc.). The invention may take the form of a computer program product,which can be embodied by a computer-usable or computer-readable storagemedium having computer-usable or computer-readable program instructions,“code” or a “computer program” embodied in the medium for use by or inconnection with the instruction execution system. In the context of thisdocument, a computer-usable or computer-readable medium may be anymedium that can contain, store, communicate, propagate, or transport theprogram for use by or in connection with the instruction executionsystem, apparatus, or device. The computer-usable or computer-readablemedium may be, for example but not limited to, an electronic, magnetic,optical, electromagnetic, infrared, or semiconductor system, apparatus,device, or propagation medium such as the Internet. Note that thecomputer-usable or computer-readable medium could even be paper oranother suitable medium upon which the program is printed, as theprogram can be electronically captured, via, for instance, opticalscanning of the paper or other medium, then compiled, interpreted, orotherwise processed in a suitable manner. The computer program productand any software and hardware described herein form the various meansfor carrying out the functions of the invention in the exampleembodiments.

Specific embodiments of an invention are disclosed herein. One ofordinary skill in the art will readily recognize that the invention mayhave other applications in other environments. In fact, many embodimentsand implementations are possible. The following claims are in no wayintended to limit the scope of the present invention to the specificembodiments described above. In addition, any recitation of “means for”is intended to evoke a means-plus-function reading of an element and aclaim, whereas, any elements that do not specifically use the recitation“means for”, are not intended to be read as means-plus-functionelements, even if the claim otherwise includes the word “means”. Itshould also be noted that although the specification lists method stepsoccurring in a particular order, these steps may be executed in anyorder, or at the same time.

1. A method for visual recognition of at least one object in an image,the method comprising: generating extremum information associated withthe image by cross-correlating at least one structure across at least aportion of the image; extracting at least one icon from the imageassociated, wherein the icon includes at least one coordinate pointassociated with the extremum information; normalizing the icon to afixed size; and storing icon information in a computer readable form,wherein the icon information includes one or more image valuesassociated with at least a portion of the icon; and the at least onecoordinate point associated with the extremum information.
 2. The methodof claim 1, wherein the extremum information includes a set ofcoordinate points for each extremum information.
 3. The method of claim2, wherein the extremum information further includes a radius for eachextremum information.
 4. The method of claim 1, further includingdetermining an angle associated with the extremum information.
 5. Themethod of claim 1, wherein the structure used to generate the extremuminformation is a cone-like structure.
 6. The method of claim 1, whereinthe structure used to generate the extremum information is atwo-dimensional Gaussian shape.
 7. The method of claim 1, wherein thestructure used to generate the extremum information has a circularcross-section.
 8. The method of claim 7, wherein the structure has apredetermined number of sizes and extremum information associated withthe image is generated for each structure size.
 9. The method of claim4, wherein extremum information associated with a structure size isnormalized by a common scaling factor.
 10. The method of claim 9,wherein extremum information associated with two or more structure sizesis normalized using different scaling factors for each structure size.11. The method of claim 1, wherein the icon information is stored in adatabase, wherein at least one entry of the database includes adescription of the object.
 12. The method of claim 1, wherein the iconinformation is stored in a descriptor vector.
 13. The method of claim 1,wherein the extremum information is generated by extracting peak valuesfrom the step of cross-correlating.
 14. A method for matching a learnedobject with a target object, the method comprising: extracting uniquepoints from the target object, wherein the unique points are generatedfrom extremum information obtained from the target image bycross-correlating at least one structure across the target image;extracting an icon from the target image corresponding to each of theunique points; normalizing the extracted icon; and determining if theextracted icon from the target images matches a learned object.
 15. Themethod of claim 14, wherein the step of determining a match includessearching a database of learned objects to determine a match.
 16. Themethod of claim 14 further including ranking objects detected in thetarget image based on a match score.
 17. The method of claim 16, whereinobjects having a higher matching score are processed prior to objectshaving a lower matching score.
 18. The method of claim 14 furtherincluding localizing the objects detected in the target image todetermine the location of the objects in the target image.
 19. Themethod of claim 18 further including finding one or more correspondingicons in the detected icons from one or more learned icons associatedwith at least one learned object.
 20. The method of claim 19 furtherincluding computing a perspective transform for all of the uniquepoints.
 21. The method of claim 20 further including transforming objectcontour points with the perspective transform.
 22. The method of claim21 further including verifying the transformed contour points bysuperimposing the transformed contour points onto the image.
 23. Themethod of claim 21 further including generating a redundancy measure.24. The method of claim 18 further including selecting a predeterminednumber of unique points and computing a perspective transform for thepredetermined number of unique points.
 25. The method of claim 24further including transforming object contour points with theperspective transform for the predetermined number of unique points. 26.The method of claim 25 further including verifying the transformedcontour points by superimposing the transformed contour points onto theimage.
 27. The method of claim 18 further including computing aperspective transform for every set of 4 unique points.
 28. The methodof claim 14, wherein at least one of the extracted icons at leastpartially overlaps with another icon, where each of the extracted iconshave a different scale.
 29. The method of claim 28, wherein theextracted icons having a larger scale than the another icon.
 30. Themethod of claim 29, wherein the larger icon has a higher ranking thanthe another icon have a smaller size.
 31. A computer implemented methodfor learning objects for use in a visual recognition system, the methodcomprising: generating extremum information associated with one or moreimages, by cross-correlating at least one structure across at least aportion of the image over a plurality of scales; extracting at least oneicon from the one or more images, wherein the icon includes at least onecoordinate point associated with the extremum information; normalizingthe icon to a fixed size; and storing icon information in a computerreadable form as a learned object, wherein the icon information includesone or more image values associated with at least a portion of the icon;and the at least one coordinate point associated with the extremuminformation.
 32. The method of claim 31, wherein the one or more imagesare captured using a plurality of perspective views and/or planes.